OpenRaiser/Intern-Atlas huggingface dataset:
HiPPO-Prophecy: State-Space Models can Provably Learn Dynamical Systems in Context
Venue: arXiv cs.LG 2025
Year: 2025

This work explores the in-context learning capabilities of State Space Models (SSMs) and presents, to the best of our knowledge, the first theoretical explanation of a possible underlying mechanism. We introduce a novel weight construction for SSMs, enabling them to predict the next state of any dynamical system after observing previous states without parameter fine-tuning. This is accomplished by extending the HiPPO framework to demonstrate that continuous SSMs can approximate the derivative of any input signal. Specifically, we find an explicit weight construction for continuous SSMs and provide an asymptotic error bound on the derivative approximation. The discretization of this continuous SSM subsequently yields a discrete SSM that predicts the next state. Finally, we demonstrate the effectiveness of our parameterization empirically. This work should be an initial step toward understanding how sequence models based on SSMs learn in context.
Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities
Venue: arXiv cs.RO 2025
Year: 2025

Multi-Agent Path Finding (MAPF) aims to arrange collision-free goal-reaching paths for a group of agents. Anytime MAPF solvers based on large neighborhood search (LNS) have gained prominence recently due to their flexibility and scalability, leading to a surge of methods, especially those leveraging machine learning, to enhance neighborhood selection. However, several pitfalls exist and hinder a comprehensive evaluation of these new methods, which mainly include: 1) Lower than actual or incorrect baseline performance; 2) Lack of a unified evaluation setting and criterion; 3) Lack of a codebase or executable model for supervised learning methods. To address these challenges, we introduce a unified evaluation framework, implement prior methods, and conduct an extensive comparison of prominent methods. Our evaluation reveals that rule-based heuristics serve as strong baselines, while current learning-based methods show no clear advantage on time efficiency or improvement capacity. Our extensive analysis also opens up new research opportunities for improving MAPF-LNS, such as targeting high-delayed agents, applying contextual algorithms, optimizing replan order and neighborhood size, where machine learning can potentially be integrated. Code and data are available at https://github.com/ChristinaTan0704/mapf-lns-unified.
Mixed-View Panorama Synthesis using Geospatially Guided Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

We introduce the task of mixed-view panorama synthesis, where the goal is to synthesize a novel panorama given a small set of input panoramas and a satellite image of the area. This contrasts with previous work which only uses input panoramas (same-view synthesis), or an input satellite image (cross-view synthesis). We argue that the mixed-view setting is the most natural to support panorama synthesis for arbitrary locations worldwide. A critical challenge is that the spatial coverage of panoramas is uneven, with few panoramas available in many regions of the world. We introduce an approach that utilizes diffusion-based modeling and an attention-based architecture for extracting information from all available input imagery. Experimental results demonstrate the effectiveness of our proposed method. In particular, our model can handle scenarios when the available panoramas are sparse or far from the location of the panorama we are attempting to synthesize. The project page is available at https://mixed-view.github.io
A Mathematical Framework and a Suite of Learning Techniques for Neural-Symbolic Systems
Venue: arXiv cs.LG 2025
Year: 2025

The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic methods. However, a unifying framework is needed to organize common NeSy modeling patterns and develop general learning approaches. In this paper, we introduce Neural-Symbolic Energy-Based Models (NeSy-EBMs), a unifying mathematical framework for discriminative and generative NeSy modeling. Importantly, NeSy-EBMs allow the derivation of general expressions for gradients of prominent learning losses, and we introduce a suite of four learning approaches that leverage methods from multiple domains, including bilevel and stochastic policy optimization. Finally, we ground the NeSy-EBM framework with Neural Probabilistic Soft Logic (NeuPSL), an open-source NeSy-EBM library designed for scalability and expressivity, facilitating the real-world application of NeSy systems. Through extensive empirical analysis across multiple datasets, we demonstrate the practical advantages of NeSy-EBMs in various tasks, including image classification, graph node labeling, autonomous vehicle situation awareness, and question answering.
Partner in Crime: Boosting Targeted Poisoning Attacks against Federated Learning
Venue: arXiv cs.CR 2025
Year: 2025

Federated Learning (FL) exposes vulnerabilities to targeted poisoning attacks that aim to cause misclassification specifically from the source class to the target class. However, using well-established defense frameworks, the poisoning impact of these attacks can be greatly mitigated. We introduce a generalized pre-training stage approach to Boost Targeted Poisoning Attacks against FL, called BoTPA. Its design rationale is to leverage the model update contributions of all data points, including ones outside of the source and target classes, to construct an Amplifier set, in which we falsify the data labels before the FL training process, as a means to boost attacks. We comprehensively evaluate the effectiveness and compatibility of BoTPA on various targeted poisoning attacks. Under data poisoning attacks, our evaluations reveal that BoTPA can achieve a median Relative Increase in Attack Success Rate (RI-ASR) between 15.3% and 36.9% across all possible source-target class combinations, with varying percentages of malicious clients, compared to its baseline. In the context of model poisoning, BoTPA attains RI-ASRs ranging from 13.3% to 94.7% in the presence of the Krum and Multi-Krum defenses, from 2.6% to 49.2% under the Median defense, and from 2.9% to 63.5% under the Flame defense.
Sim-to-Real Domain Adaptation for Deformation Classification
Venue: arXiv cs.CV 2025
Year: 2025

Deformation detection is vital for enabling accurate assessment and prediction of structural changes in materials, ensuring timely and effective interventions to maintain safety and integrity. Automating deformation detection through computer vision is crucial for efficient monitoring, but it faces significant challenges in creating a comprehensive dataset of both deformed and non-deformed objects, which can be difficult to obtain in many scenarios. In this paper, we introduce a novel framework for generating controlled synthetic data that simulates deformed objects. This approach allows for the realistic modeling of object deformations under various conditions. Our framework integrates an intelligent adapter network that facilitates sim-to-real domain adaptation, enhancing classification results without requiring real data from deformed objects. We conduct experiments on domain adaptation and classification tasks and demonstrate that our framework improves sim-to-real classification results compared to simulation baseline.
LLaMAR: Long-Horizon Planning for Multi-Agent Robots in Partially Observable Environments
Venue: arXiv cs.RO 2025
Year: 2025

The ability of Language Models (LMs) to understand natural language makes them a powerful tool for parsing human instructions into task plans for autonomous robots. Unlike traditional planning methods that rely on domain-specific knowledge and handcrafted rules, LMs generalize from diverse data and adapt to various tasks with minimal tuning, acting as a compressed knowledge base. However, LMs in their standard form face challenges with long-horizon tasks, particularly in partially observable multi-agent settings. We propose an LM-based Long-Horizon Planner for Multi-Agent Robotics (LLaMAR), a cognitive architecture for planning that achieves state-of-the-art results in long-horizon tasks within partially observable environments. LLaMAR employs a plan-act-correct-verify framework, allowing self-correction from action execution feedback without relying on oracles or simulators. Additionally, we present MAP-THOR, a comprehensive test suite encompassing household tasks of varying complexity within the AI2-THOR environment. Experiments show that LLaMAR achieves a 30% higher success rate than other state-of-the-art LM-based multi-agent planners in MAP-THOR and Search \& Rescue tasks. Code can be found at https://github.com/nsidn98/LLaMAR
Work From Home and Privacy Challenges: What Do Workers Face and What are They Doing About it?
Venue: arXiv cs.HC 2025
Year: 2025

The COVID-19 pandemic has reshaped the way people work, normalizing the practice of working from home. However, work from home (WFH) can cause a blurring of personal and professional boundaries, surfacing new privacy issues, especially when workers take work meetings from their homes. As WFH arrangements are now standard practice in many organizations, addressing the associated privacy concerns should be a key part of creating healthy work environments for workers. To this end, we conducted a scenario-based survey with 214 US-based workers who currently work from home regularly. Our results suggest that privacy invasions are commonly experienced while working from home and cause discomfort to many workers. However, only a minority said that the discomfort escalated to cause harm to them or others and that the harm was almost always minor and psychological. While scenarios that restrict worker autonomy (prohibit turning off camera or microphone) are the least experienced scenarios, they are associated with the highest reported discomfort. In addition, participants reported measures that violated or would violate their employer's autonomy-restricting rules to protect their privacy. We also find that conference tool settings that can prevent privacy invasions are not widely used compared to manual privacy-protective measures. Our findings provide a better understanding of the privacy challenges landscape that WFH workers face and how they address them, providing useful insights to organizations' policymakers and technology designers for areas of improvements, to provide healthier work environments to workers.
Strategic Integration of Artificial Intelligence in the C-Suite: The Role of the Chief AI Officer
Venue: arXiv cs.CY 2025
Year: 2025

The integration of Artificial Intelligence (AI) into corporate strategy has become critical for organizations seeking to maintain a competitive advantage in the digital age. As AI transforms business models, operations, and decision-making, the need for dedicated executive leadership to guide, govern, and orchestrate this transformation becomes increasingly evident. This paper examines emerging future scenarios across three domains: the AI Economy, the AI Organization, and Competition in the Age of AI. These domains reveal environmental, structural, and strategic tensions that existing C-suite roles struggle to resolve. In response, the paper develops a theory-informed framework for the Chief AI Officer (CAIO), outlining the distinct functions and capabilities required to guide and govern AI at scale. Drawing on illustrative cases and emerging practice, this conceptualization clarifies the CAIOs unique role within the executive landscape and presents a forward-looking research agenda. This paper advances the discourse on AI leadership by offering a theory-driven rationale for the strategic integration of AI at the executive level and by positioning the Chief AI Officer as a distinct and necessary role within modern organizations.
Public Opinions About Copyright for AI-Generated Art: The Role of Egocentricity, Competition, and Experience
Venue: arXiv cs.CY 2025
Year: 2025

Breakthroughs in generative AI (GenAI) have fueled debates concerning the artistic and legal status of AI-generated creations. We investigate laypeople's perceptions ($N$$=$$432$) of AI-generated art through the lens of copyright law. We study lay judgments of GenAI images concerning several copyright-related factors and capture people's opinions of who should be the authors and rights-holders of AI-generated images. To do so, we held an incentivized AI art competition in which some participants used a GenAI model to create art while others evaluated these images. We find that participants believe creativity and effort, but not skills, are needed to create AI-generated art. Participants were most likely to attribute authorship and copyright to the AI model's users and to the artists whose creations were used for training. We find evidence of egocentric effects: participants favored their own art with respect to quality, creativity, and effort -- particularly when these assessments determined real monetary awards.
Fluid Antenna Multiple Access Assisted Integrated Data and Energy Transfer: Outage and Multiplexing Gain Analysis
Venue: arXiv cs.IT 2025
Year: 2025

Fluid antenna multiple access (FAMA) exploits the spatial opportunities in wireless channels to overcome multiuser interference by position (a.k.a.~port) switching, which can achieve better performance compared to traditional fixed multiple-input multiple-output (MIMO) systems. Additionally, integrated data and energy transfer (IDET) is capable of providing both wireless data transfer (WDT) and wireless energy transfer (WET) services towards low-power devices. In this paper, a FAMA-assisted IDET system is investigated, where a base station (BS) equipped with $N$ fixed antennas provides dedicated IDET services towards $N$ user equipments (UEs). Each UE is equipped with a single fluid antenna, while the power splitting (PS) approach is conceived for coordinating WDT and WET. The outage probabilities of both WDT and WET are derived and approximated into closed-forms, where the fluid antenna (FA) at each UE selects the optimal port to achieve the maximum signal-to-interference-plus-noise ratio (SINR) or the energy harvesting power (EHP). The IDET outage probabilities are defined and subsequently derived and approximated into closed-forms. Further, multiplexing gains of the proposed system are defined and analyzed to evaluate the performace. Numerical results validate the theoretical analysis, while also illustrate that the trade-off is achieved between WDT and WET performance by exploiting different port selection strategies. Furthermore, the number of UEs should be optimized to achieve better IDET performance of the system.
Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews
Venue: arXiv cs.LG 2025
Year: 2025

Systematic literature reviews (SLRs) are essential but labor-intensive due to high publication volumes and inefficient keyword-based filtering. To streamline this process, we evaluate Large Language Models (LLMs) for enhancing efficiency and accuracy in corpus filtration while minimizing manual effort. Our open-source tool LLMSurver presents a visual interface to utilize LLMs for literature filtration, evaluate the results, and refine queries in an interactive way. We assess the real-world performance of our approach in filtering over 8.3k articles during a recent survey construction, comparing results with human efforts. The findings show that recent LLM models can reduce filtering time from weeks to minutes. A consensus scheme ensures recall rates >98.8%, surpassing typical human error thresholds and improving selection accuracy. This work advances literature review methodologies and highlights the potential of responsible human-AI collaboration in academic research.
MARTSIA: A Tool for Confidential Data Exchange via Public Blockchain
Venue: arXiv cs.CR 2025
Year: 2025

Blockchain technology streamlines multi-party collaborations in decentralized settings, especially when trust is limited or difficult to establish. While public blockchains enhance transparency and reliability by replicating data across all network nodes, they also conflict with confidentiality. Here, we introduce Multi-Authority Approach to Transaction Systems for Interoperating Applications (MARTSIA) to address this challenge. MARTSIA provides fine-grained read-access control at the message-part level by combining user-defined policies with certifier-declared attributes. The approach guarantees that even though data is replicated across the network to maintain consistency, fault tolerance, and availability, its confidentiality is securely preserved through encryption. To this end, MARTSIA integrates blockchain technologies, Multi-Authority Attribute-Based Encryption, and distributed hash-table file storages. This architecture effectively balances the transparency inherent in public blockchains with the privacy required for sensitive applications. We present the tool and its applicability in a business scenario.
Rethinking OWL Expressivity: Semantic Units for FAIR and Cognitively Interoperable Knowledge Graphs Why OWLs don't have to understand everything they say
Venue: arXiv cs.DB 2025
Year: 2025

Semantic knowledge graphs are foundational to implementing the FAIR Principles, yet RDF/OWL representations often lack the semantic flexibility and cognitive interoperability required in scientific domains. We present a novel framework for semantic modularization based on semantic units (i.e., modular, semantically coherent subgraphs enhancing expressivity, reusability, and interpretability), combined with four new representational resource types (some-instance, most-instances, every-instance, all-instances) for modelling assertional, contingent, prototypical, and universal statements. The framework enables the integration of knowledge modelled using different logical frameworks (e.g., OWL, First-Order Logic, or none), provided each semantic unit is internally consistent and annotated with its logic base. This allows, for example, querying all OWL 2.0-compliant units for reasoning purposes while preserving the full graph for broader knowledge discovery. Our framework addresses twelve core limitations of OWL/RDF modeling, including negation, cardinality, complex class axioms, conditional and directive statements, and logical arguments, while improving cognitive accessibility for domain experts. We provide schemata and translation patterns to demonstrate semantic interoperability and reasoning potential, establishing a scalable foundation for constructing FAIR-aligned, semantically rich knowledge graphs.
AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler
Venue: arXiv cs.LG 2025
Year: 2025

In real-world scenarios, tabular data often suffer from distribution shifts that threaten the performance of machine learning models. Despite its prevalence and importance, handling distribution shifts in the tabular domain remains underexplored due to the inherent challenges within the tabular data itself. In this sense, test-time adaptation (TTA) offers a promising solution by adapting models to target data without accessing source data, crucial for privacy-sensitive tabular domains. However, existing TTA methods either 1) overlook the nature of tabular distribution shifts, often involving label distribution shifts, or 2) impose architectural constraints on the model, leading to a lack of applicability. To this end, we propose AdapTable, a novel TTA framework for tabular data. AdapTable operates in two stages: 1) calibrating model predictions using a shift-aware uncertainty calibrator, and 2) adjusting these predictions to match the target label distribution with a label distribution handler. We validate the effectiveness of AdapTable through theoretical analysis and extensive experiments on various distribution shift scenarios. Our results demonstrate AdapTable's ability to handle various real-world distribution shifts, achieving up to a 16% improvement on the HELOC dataset.
FabGPT: An Efficient Large Multimodal Model for Complex Wafer Defect Knowledge Queries
Venue: arXiv cs.CV 2025
Year: 2025

Intelligence is key to advancing integrated circuit (IC) fabrication. Recent breakthroughs in Large Multimodal Models (LMMs) have unlocked extraditionary abilities in understanding images and text, fostering intelligent fabrication. Leveraging the power of LMMs, we introduce FabGPT, a customized IC fabrication large multimodal model for wafer defect knowledge query. FabGPT manifests expertise in conducting defect detection in Scanning Electron Microscope (SEM) images, performing root cause analysis, and providing expert Q&A on fabrication processes. FabGPT matches enhanced multimodal features to automatically detect minute defects under complex wafer backgrounds and reduce the subjectivity of manual threshold settings. Besides, the proposed modulation module and interactive corpus training strategy embed wafer defect knowledge into the pre-trained model, effectively balancing Q&A queries related to defect knowledge and original knowledge and mitigating the modality bias issues. Experiments on in-house fab data show that FabGPT achieves significant performance improvement in wafer defect detection and knowledge querying.
Naturally Occurring Feedback is Common, Extractable and Useful
Venue: arXiv cs.CL 2025
Year: 2025

Human feedback data is a critical component in developing language models. However, collecting this feedback is costly and ultimately not scalable. Inspired by the way human interlocutors provide spontaneous unsolicited feedback to each other, we propose to extract feedback that users naturally include when interacting with chat models. We manually annotated conversations to confirm the presence of naturally occurring feedback in a standard corpus, finding that as much as 30% of the chats include explicit feedback. Comparing to older datasets, we find that naturally occurring feedback is more prevalent in recent conversation datasets, suggesting that more than ever, naturally occurring feedback can serve as a valuable resource for feedback data. We propose a method for automatically extracting this feedback, and apply it to over 1M conversations to obtain hundreds of thousands of feedback samples. The extracted feedback shows promise: training with it improves over baseline models and enhances model alignment to human preferences.
Fast Matrix Multiplications for Lookup Table-Quantized LLMs
Venue: arXiv cs.LG 2025
Year: 2025

The deployment of large language models (LLMs) is often constrained by memory bandwidth, where the primary bottleneck is the cost of transferring model parameters from the GPU's global memory to its registers. When coupled with custom kernels that fuse the dequantization and matmul operations, weight-only quantization can thus enable faster inference by reducing the amount of memory movement. However, developing high-performance kernels for weight-quantized LLMs presents substantial challenges, especially when the weights are compressed to non-evenly-divisible bit widths (e.g., 3 bits) with non-uniform, lookup table (LUT) quantization. This paper describes FLUTE, a flexible lookup table engine for LUT-quantized LLMs, which uses offline restructuring of the quantized weight matrix to minimize bit manipulations associated with unpacking, and vectorization and duplication of the lookup table to mitigate shared memory bandwidth constraints. At batch sizes < 32 and quantization group size of 128 (typical in LLM inference), the FLUTE kernel can be 2-4x faster than existing GEMM kernels. As an application of FLUTE, we explore a simple extension to lookup table-based NormalFloat quantization and apply it to quantize LLaMA3 to various configurations, obtaining competitive quantization performance against strong baselines while obtaining an end-to-end throughput increase of 1.5 to 2 times.